Overview

Dataset statistics

Number of variables27
Number of observations4061127
Missing cells4364217
Missing cells (%)4.0%
Duplicate rows6391
Duplicate rows (%)0.2%
Total size in memory867.6 MiB
Average record size in memory224.0 B

Variable types

Categorical8
DateTime2
Numeric16
Unsupported1

Alerts

Dataset has 6391 (0.2%) duplicate rowsDuplicates
VIN has a high cardinality: 3405439 distinct valuesHigh cardinality
TypMot has a high cardinality: 60190 distinct valuesHigh cardinality
TZn has a high cardinality: 6676 distinct valuesHigh cardinality
ObchOznTyp has a high cardinality: 72999 distinct valuesHigh cardinality
Ct has a high cardinality: 142 distinct valuesHigh cardinality
DrTP is highly imbalanced (63.7%)Imbalance
TZn is highly imbalanced (59.3%)Imbalance
DrVoz is highly imbalanced (71.7%)Imbalance
Ct is highly imbalanced (73.0%)Imbalance
VyslSTK is highly imbalanced (76.3%)Imbalance
TypMot has 220108 (5.4%) missing valuesMissing
VyslEmise has 4061127 (100.0%) missing valuesMissing
Zav9 is highly skewed (γ1 = 53.93148962)Skewed
VIN is uniformly distributedUniform
VyslEmise is an unsupported type, check if it needs cleaning or further analysisUnsupported
Km has 333034 (8.2%) zerosZeros
ZavA has 2004573 (49.4%) zerosZeros
ZavB has 3805454 (93.7%) zerosZeros
ZavC has 4026648 (99.2%) zerosZeros
Zav0 has 3777381 (93.0%) zerosZeros
Zav1 has 2972682 (73.2%) zerosZeros
Zav2 has 3745528 (92.2%) zerosZeros
Zav3 has 3760669 (92.6%) zerosZeros
Zav4 has 3132361 (77.1%) zerosZeros
Zav5 has 2934360 (72.3%) zerosZeros
Zav6 has 2370789 (58.4%) zerosZeros
Zav7 has 4009463 (98.7%) zerosZeros
Zav8 has 4021365 (99.0%) zerosZeros
Zav9 has 4058109 (99.9%) zerosZeros

Reproduction

Analysis started2023-04-01 08:24:10.807927
Analysis finished2023-04-01 08:27:04.540587
Duration2 minutes and 53.73 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

DrTP
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.0 MiB
pravidelná
2763040 
Evidenční kontrola
869644 
opakovaná
 
200559
Před registrací
 
165393
Technická silniční kontrola
 
28020
Other values (9)
 
34471

Length

Max length46
Median length10
Mean length12.073054
Min length3

Characters and Unicode

Total characters49030207
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpravidelná
2nd rowpravidelná
3rd rowpravidelná
4th rowpravidelná
5th rowpravidelná

Common Values

ValueCountFrequency (%)
pravidelná 2763040
68.0%
Evidenční kontrola 869644
 
21.4%
opakovaná 200559
 
4.9%
Před registrací 165393
 
4.1%
Technická silniční kontrola 28020
 
0.7%
Na žádost zákazníka 18412
 
0.5%
Před schvál. tech. způsob. vozidla 5979
 
0.1%
ADR 4580
 
0.1%
Před registrací - opakovaná 3629
 
0.1%
TSK - Opakovaná po DN 757
 
< 0.1%
Other values (4) 1114
 
< 0.1%

Length

2023-04-01T10:27:04.584459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pravidelná 2763040
52.8%
kontrola 897664
 
17.2%
evidenční 869644
 
16.6%
opakovaná 205977
 
3.9%
před 175170
 
3.3%
registrací 169022
 
3.2%
technická 28102
 
0.5%
silniční 28020
 
0.5%
na 18412
 
0.4%
žádost 18412
 
0.4%
Other values (12) 56300
 
1.1%

Most occurring characters

ValueCountFrequency (%)
n 5708605
11.6%
a 4303228
8.8%
e 4011208
8.2%
r 3998830
8.2%
i 3891996
7.9%
v 3850957
 
7.9%
d 3832496
 
7.8%
l 3701102
 
7.5%
á 3040173
 
6.2%
p 2976004
 
6.1%
Other values (27) 9715608
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46724872
95.3%
Space Separator 1168636
 
2.4%
Uppercase Letter 1112837
 
2.3%
Other Punctuation 18444
 
< 0.1%
Dash Punctuation 5418
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5708605
12.2%
a 4303228
9.2%
e 4011208
8.6%
r 3998830
8.6%
i 3891996
8.3%
v 3850957
8.2%
d 3832496
8.2%
l 3701102
7.9%
á 3040173
6.5%
p 2976004
6.4%
Other values (14) 7410273
15.9%
Uppercase Letter
ValueCountFrequency (%)
E 869644
78.1%
P 175170
 
15.7%
T 29415
 
2.6%
N 19251
 
1.7%
D 5562
 
0.5%
A 4805
 
0.4%
R 4805
 
0.4%
S 1395
 
0.1%
K 1395
 
0.1%
O 1395
 
0.1%
Space Separator
ValueCountFrequency (%)
1168636
100.0%
Other Punctuation
ValueCountFrequency (%)
. 18444
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47837709
97.6%
Common 1192498
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5708605
11.9%
a 4303228
9.0%
e 4011208
8.4%
r 3998830
8.4%
i 3891996
8.1%
v 3850957
8.1%
d 3832496
8.0%
l 3701102
7.7%
á 3040173
 
6.4%
p 2976004
 
6.2%
Other values (24) 8523110
17.8%
Common
ValueCountFrequency (%)
1168636
98.0%
. 18444
 
1.5%
- 5418
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43807296
89.3%
None 5222911
 
10.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5708605
13.0%
a 4303228
9.8%
e 4011208
9.2%
r 3998830
9.1%
i 3891996
8.9%
v 3850957
8.8%
d 3832496
8.7%
l 3701102
8.4%
p 2976004
6.8%
o 2237434
 
5.1%
Other values (21) 5295436
12.1%
None
ValueCountFrequency (%)
á 3040173
58.2%
í 1085262
 
20.8%
č 897664
 
17.2%
ř 175252
 
3.4%
ž 18412
 
0.4%
ů 6148
 
0.1%

VIN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3405439
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size62.0 MiB
0
 
53
-
 
42
003
 
29
027
 
25
008
 
24
Other values (3405434)
4060954 

Length

Max length21
Median length17
Mean length16.47427
Min length1

Characters and Unicode

Total characters66904103
Distinct characters48
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2858026 ?
Unique (%)70.4%

Sample

1st row7857
2nd row11666
3rd row919327
4th row1147
5th row731713

Common Values

ValueCountFrequency (%)
0 53
 
< 0.1%
- 42
 
< 0.1%
003 29
 
< 0.1%
027 25
 
< 0.1%
008 24
 
< 0.1%
013 24
 
< 0.1%
004 23
 
< 0.1%
005 22
 
< 0.1%
001 21
 
< 0.1%
007 21
 
< 0.1%
Other values (3405429) 4060843
> 99.9%

Length

2023-04-01T10:27:04.785551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 53
 
< 0.1%
47
 
< 0.1%
003 29
 
< 0.1%
027 25
 
< 0.1%
008 24
 
< 0.1%
013 24
 
< 0.1%
004 23
 
< 0.1%
005 22
 
< 0.1%
001 21
 
< 0.1%
007 21
 
< 0.1%
Other values (3405351) 4060899
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 6796622
 
10.2%
1 5315071
 
7.9%
2 4200838
 
6.3%
3 3861816
 
5.8%
5 3494315
 
5.2%
6 3459489
 
5.2%
4 3408615
 
5.1%
7 3117957
 
4.7%
8 2911795
 
4.4%
Z 2813004
 
4.2%
Other values (38) 27524581
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39224370
58.6%
Uppercase Letter 27605310
41.3%
Dash Punctuation 40840
 
0.1%
Other Punctuation 33514
 
0.1%
Space Separator 63
 
< 0.1%
Math Symbol 3
 
< 0.1%
Connector Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 2813004
 
10.2%
B 2168169
 
7.9%
W 1899558
 
6.9%
F 1797359
 
6.5%
M 1742376
 
6.3%
A 1732875
 
6.3%
T 1701402
 
6.2%
V 1348556
 
4.9%
J 1180946
 
4.3%
C 1081169
 
3.9%
Other values (16) 10139896
36.7%
Decimal Number
ValueCountFrequency (%)
0 6796622
17.3%
1 5315071
13.6%
2 4200838
10.7%
3 3861816
9.8%
5 3494315
8.9%
6 3459489
8.8%
4 3408615
8.7%
7 3117957
7.9%
8 2911795
7.4%
9 2657852
 
6.8%
Other Punctuation
ValueCountFrequency (%)
/ 32430
96.8%
. 726
 
2.2%
% 288
 
0.9%
* 59
 
0.2%
, 11
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 2
66.7%
~ 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 40840
100.0%
Space Separator
ValueCountFrequency (%)
63
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39298793
58.7%
Latin 27605310
41.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 2813004
 
10.2%
B 2168169
 
7.9%
W 1899558
 
6.9%
F 1797359
 
6.5%
M 1742376
 
6.3%
A 1732875
 
6.3%
T 1701402
 
6.2%
V 1348556
 
4.9%
J 1180946
 
4.3%
C 1081169
 
3.9%
Other values (16) 10139896
36.7%
Common
ValueCountFrequency (%)
0 6796622
17.3%
1 5315071
13.5%
2 4200838
10.7%
3 3861816
9.8%
5 3494315
8.9%
6 3459489
8.8%
4 3408615
8.7%
7 3117957
7.9%
8 2911795
7.4%
9 2657852
 
6.8%
Other values (12) 74423
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66904103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6796622
 
10.2%
1 5315071
 
7.9%
2 4200838
 
6.3%
3 3861816
 
5.8%
5 3494315
 
5.2%
6 3459489
 
5.2%
4 3408615
 
5.1%
7 3117957
 
4.7%
8 2911795
 
4.4%
Z 2813004
 
4.2%
Other values (38) 27524581
41.1%
Distinct365
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.0 MiB
Minimum2020-01-01 00:00:00
Maximum2020-12-31 00:00:00
2023-04-01T10:27:04.885807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:27:04.980130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TypMot
Categorical

HIGH CARDINALITY  MISSING 

Distinct60190
Distinct (%)1.6%
Missing220108
Missing (%)5.4%
Memory size62.0 MiB
-
 
104560
BXE
 
37725
ALH
 
34744
CJZ
 
31898
CBZA
 
26519
Other values (60185)
3605573 

Length

Max length17
Median length16
Mean length4.5116806
Min length1

Characters and Unicode

Total characters17329451
Distinct characters105
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29699 ?
Unique (%)0.8%

Sample

1st row667TA/MEK
2nd rowZ 5201
3rd rowZ 4001
4th rowZ 3001
5th row781.136

Common Values

ValueCountFrequency (%)
- 104560
 
2.6%
BXE 37725
 
0.9%
ALH 34744
 
0.9%
CJZ 31898
 
0.8%
CBZA 26519
 
0.7%
G4FA 26469
 
0.7%
AQW 25297
 
0.6%
ASV 24423
 
0.6%
781.136M 23970
 
0.6%
BLS 22942
 
0.6%
Other values (60180) 3482472
85.8%
(Missing) 220108
 
5.4%

Length

2023-04-01T10:27:05.084183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
110574
 
2.6%
bxe 37728
 
0.9%
alh 34785
 
0.8%
7 32832
 
0.8%
cjz 31901
 
0.7%
cbza 26522
 
0.6%
g4fa 26472
 
0.6%
m 26003
 
0.6%
aqw 25459
 
0.6%
asv 24450
 
0.6%
Other values (44985) 3931539
91.3%

Most occurring characters

ValueCountFrequency (%)
A 1314858
 
7.6%
1 1058827
 
6.1%
B 922785
 
5.3%
F 869205
 
5.0%
4 840849
 
4.9%
C 815862
 
4.7%
0 803165
 
4.6%
D 795362
 
4.6%
2 593618
 
3.4%
6 550525
 
3.2%
Other values (95) 8764395
50.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10371910
59.9%
Decimal Number 5961775
34.4%
Space Separator 468858
 
2.7%
Other Punctuation 302611
 
1.7%
Dash Punctuation 218170
 
1.3%
Math Symbol 2136
 
< 0.1%
Open Punctuation 1347
 
< 0.1%
Lowercase Letter 1327
 
< 0.1%
Close Punctuation 1310
 
< 0.1%
Modifier Symbol 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1314858
 
12.7%
B 922785
 
8.9%
F 869205
 
8.4%
C 815862
 
7.9%
D 795362
 
7.7%
E 496425
 
4.8%
H 493133
 
4.8%
M 468340
 
4.5%
Z 391995
 
3.8%
K 373864
 
3.6%
Other values (31) 3430081
33.1%
Lowercase Letter
ValueCountFrequency (%)
a 160
 
12.1%
c 128
 
9.6%
b 104
 
7.8%
d 87
 
6.6%
f 85
 
6.4%
x 64
 
4.8%
s 57
 
4.3%
h 54
 
4.1%
z 51
 
3.8%
l 48
 
3.6%
Other values (21) 489
36.9%
Other Punctuation
ValueCountFrequency (%)
. 252084
83.3%
/ 34826
 
11.5%
, 9107
 
3.0%
* 6360
 
2.1%
? 166
 
0.1%
; 33
 
< 0.1%
: 23
 
< 0.1%
" 7
 
< 0.1%
& 2
 
< 0.1%
% 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1058827
17.8%
4 840849
14.1%
0 803165
13.5%
2 593618
10.0%
6 550525
9.2%
3 478566
8.0%
7 460616
7.7%
8 425776
7.1%
9 395318
 
6.6%
5 354515
 
5.9%
Modifier Symbol
ValueCountFrequency (%)
¨ 3
50.0%
´ 2
33.3%
˙ 1
 
16.7%
Math Symbol
ValueCountFrequency (%)
+ 2135
> 99.9%
| 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1309
99.9%
] 1
 
0.1%
Space Separator
ValueCountFrequency (%)
468858
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 218170
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1347
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10373237
59.9%
Common 6956214
40.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1314858
 
12.7%
B 922785
 
8.9%
F 869205
 
8.4%
C 815862
 
7.9%
D 795362
 
7.7%
E 496425
 
4.8%
H 493133
 
4.8%
M 468340
 
4.5%
Z 391995
 
3.8%
K 373864
 
3.6%
Other values (62) 3431408
33.1%
Common
ValueCountFrequency (%)
1 1058827
15.2%
4 840849
12.1%
0 803165
11.5%
2 593618
8.5%
6 550525
7.9%
3 478566
6.9%
468858
6.7%
7 460616
6.6%
8 425776
6.1%
9 395318
 
5.7%
Other values (23) 880096
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17324111
> 99.9%
None 5339
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1314858
 
7.6%
1 1058827
 
6.1%
B 922785
 
5.3%
F 869205
 
5.0%
4 840849
 
4.9%
C 815862
 
4.7%
0 803165
 
4.6%
D 795362
 
4.6%
2 593618
 
3.4%
6 550525
 
3.2%
Other values (72) 8759055
50.6%
None
ValueCountFrequency (%)
Š 3932
73.6%
Č 854
 
16.0%
Á 183
 
3.4%
Ý 113
 
2.1%
Ř 59
 
1.1%
Í 49
 
0.9%
Ž 41
 
0.8%
š 31
 
0.6%
ý 24
 
0.4%
Ě 19
 
0.4%
Other values (12) 34
 
0.6%
Modifier Letters
ValueCountFrequency (%)
˙ 1
100.0%

TZn
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct6676
Distinct (%)0.2%
Missing6756
Missing (%)0.2%
Memory size62.0 MiB
ŠKODA
1007831 
FORD
268120 
VOLKSWAGEN
242345 
RENAULT
 
187490
PEUGEOT
 
183588
Other values (6671)
2164997 

Length

Max length30
Median length29
Mean length5.8631201
Min length1

Characters and Unicode

Total characters23771264
Distinct characters113
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3092 ?
Unique (%)0.1%

Sample

1st rowBSS
2nd rowSTS
3rd rowBSS
4th rowBSS
5th rowBSS

Common Values

ValueCountFrequency (%)
ŠKODA 1007831
24.8%
FORD 268120
 
6.6%
VOLKSWAGEN 242345
 
6.0%
RENAULT 187490
 
4.6%
PEUGEOT 183588
 
4.5%
VW 144858
 
3.6%
CITROËN 134809
 
3.3%
MERCEDES-BENZ 123284
 
3.0%
OPEL 113148
 
2.8%
HYUNDAI 111146
 
2.7%
Other values (6666) 1537752
37.9%

Length

2023-04-01T10:27:05.190205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
škoda 1007862
24.1%
ford 268137
 
6.4%
volkswagen 242345
 
5.8%
renault 187589
 
4.5%
peugeot 183598
 
4.4%
vw 144859
 
3.5%
citroën 134809
 
3.2%
mercedes-benz 123292
 
3.0%
opel 113148
 
2.7%
hyundai 111148
 
2.7%
Other values (6268) 1657652
39.7%

Most occurring characters

ValueCountFrequency (%)
A 2885105
 
12.1%
O 2653500
 
11.2%
D 1862771
 
7.8%
E 1792620
 
7.5%
K 1447767
 
6.1%
N 1155478
 
4.9%
R 1086681
 
4.6%
T 1064159
 
4.5%
Š 1010868
 
4.3%
S 907867
 
3.8%
Other values (103) 7904448
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 23479446
98.8%
Space Separator 135149
 
0.6%
Dash Punctuation 132458
 
0.6%
Lowercase Letter 10199
 
< 0.1%
Other Punctuation 7264
 
< 0.1%
Decimal Number 6568
 
< 0.1%
Math Symbol 152
 
< 0.1%
Close Punctuation 11
 
< 0.1%
Modifier Symbol 8
 
< 0.1%
Open Punctuation 8
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2885105
 
12.3%
O 2653500
 
11.3%
D 1862771
 
7.9%
E 1792620
 
7.6%
K 1447767
 
6.2%
N 1155478
 
4.9%
R 1086681
 
4.6%
T 1064159
 
4.5%
Š 1010868
 
4.3%
S 907867
 
3.9%
Other values (38) 7612630
32.4%
Lowercase Letter
ValueCountFrequency (%)
a 1255
12.3%
n 1078
 
10.6%
o 1031
 
10.1%
e 958
 
9.4%
r 705
 
6.9%
k 481
 
4.7%
l 443
 
4.3%
c 433
 
4.2%
m 423
 
4.1%
s 418
 
4.1%
Other values (30) 2974
29.2%
Decimal Number
ValueCountFrequency (%)
1 1449
22.1%
0 1242
18.9%
5 1217
18.5%
2 788
12.0%
3 525
 
8.0%
7 479
 
7.3%
6 389
 
5.9%
8 218
 
3.3%
4 180
 
2.7%
9 81
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 6519
89.7%
, 276
 
3.8%
& 234
 
3.2%
/ 222
 
3.1%
* 9
 
0.1%
§ 2
 
< 0.1%
: 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 132452
> 99.9%
6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
135149
100.0%
Math Symbol
ValueCountFrequency (%)
+ 152
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23489645
98.8%
Common 281619
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2885105
 
12.3%
O 2653500
 
11.3%
D 1862771
 
7.9%
E 1792620
 
7.6%
K 1447767
 
6.2%
N 1155478
 
4.9%
R 1086681
 
4.6%
T 1064159
 
4.5%
Š 1010868
 
4.3%
S 907867
 
3.9%
Other values (78) 7622829
32.5%
Common
ValueCountFrequency (%)
135149
48.0%
- 132452
47.0%
. 6519
 
2.3%
1 1449
 
0.5%
0 1242
 
0.4%
5 1217
 
0.4%
2 788
 
0.3%
3 525
 
0.2%
7 479
 
0.2%
6 389
 
0.1%
Other values (15) 1410
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22524998
94.8%
None 1246260
 
5.2%
Punctuation 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2885105
12.8%
O 2653500
 
11.8%
D 1862771
 
8.3%
E 1792620
 
8.0%
K 1447767
 
6.4%
N 1155478
 
5.1%
R 1086681
 
4.8%
T 1064159
 
4.7%
S 907867
 
4.0%
I 907628
 
4.0%
Other values (64) 6761422
30.0%
None
ValueCountFrequency (%)
Š 1010868
81.1%
Ë 134812
 
10.8%
Í 32407
 
2.6%
Ý 31305
 
2.5%
Ü 12602
 
1.0%
Á 6496
 
0.5%
Ö 6068
 
0.5%
Č 5003
 
0.4%
Ě 1571
 
0.1%
Ř 1475
 
0.1%
Other values (28) 3653
 
0.3%
Punctuation
ValueCountFrequency (%)
6
100.0%

DrVoz
Categorical

Distinct46
Distinct (%)< 0.1%
Missing1224
Missing (%)< 0.1%
Memory size62.0 MiB
OSOBNÍ AUTOMOBIL
2976634 
NÁKLADNÍ AUTOMOBIL
490273 
NÁKLADNÍ PŘÍVĚS
 
171431
MOTOCYKL
 
158746
NÁKLADNÍ NÁVĚS
 
51381
Other values (41)
 
211438

Length

Max length30
Median length16
Mean length15.814862
Min length5

Characters and Unicode

Total characters64206807
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowPŘÍVĚS TRAKTOROVÝ
2nd rowPŘÍVĚS TRAKTOROVÝ
3rd rowPŘÍVĚS TRAKTOROVÝ
4th rowPŘÍVĚS TRAKTOROVÝ
5th rowPŘÍVĚS TRAKTOROVÝ

Common Values

ValueCountFrequency (%)
OSOBNÍ AUTOMOBIL 2976634
73.3%
NÁKLADNÍ AUTOMOBIL 490273
 
12.1%
NÁKLADNÍ PŘÍVĚS 171431
 
4.2%
MOTOCYKL 158746
 
3.9%
NÁKLADNÍ NÁVĚS 51381
 
1.3%
PŘÍPOJNÉ VOZIDLO 39972
 
1.0%
TRAKTOR KOLOVÝ 24433
 
0.6%
PŘÍVĚS TRAKTOROVÝ 21506
 
0.5%
TRAKTOR 20919
 
0.5%
AUTOBUS 19796
 
0.5%
Other values (36) 84812
 
2.1%

Length

2023-04-01T10:27:05.287000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
automobil 3485377
43.8%
osobní 2976634
37.4%
nákladní 720238
 
9.1%
přívěs 201954
 
2.5%
motocykl 158746
 
2.0%
vozidlo 63914
 
0.8%
návěs 59596
 
0.7%
traktor 45410
 
0.6%
přípojné 39972
 
0.5%
speciální 27686
 
0.3%
Other values (42) 169085
 
2.1%

Most occurring characters

ValueCountFrequency (%)
O 13621138
21.2%
B 6482437
10.1%
N 4608341
 
7.2%
L 4502923
 
7.0%
A 4346578
 
6.8%
Í 4014794
 
6.3%
3888714
 
6.1%
T 3852542
 
6.0%
M 3650179
 
5.7%
I 3577022
 
5.6%
Other values (24) 11662139
18.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60313960
93.9%
Space Separator 3888714
 
6.1%
Other Punctuation 4132
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 13621138
22.6%
B 6482437
10.7%
N 4608341
 
7.6%
L 4502923
 
7.5%
A 4346578
 
7.2%
Í 4014794
 
6.7%
T 3852542
 
6.4%
M 3650179
 
6.1%
I 3577022
 
5.9%
U 3548337
 
5.9%
Other values (21) 8109669
13.4%
Space Separator
ValueCountFrequency (%)
3888714
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4132
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 60313960
93.9%
Common 3892847
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 13621138
22.6%
B 6482437
10.7%
N 4608341
 
7.6%
L 4502923
 
7.5%
A 4346578
 
7.2%
Í 4014794
 
6.7%
T 3852542
 
6.4%
M 3650179
 
6.1%
I 3577022
 
5.9%
U 3548337
 
5.9%
Other values (21) 8109669
13.4%
Common
ValueCountFrequency (%)
3888714
99.9%
. 4132
 
0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58671927
91.4%
None 5534880
 
8.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 13621138
23.2%
B 6482437
11.0%
N 4608341
 
7.9%
L 4502923
 
7.7%
A 4346578
 
7.4%
3888714
 
6.6%
T 3852542
 
6.6%
M 3650179
 
6.2%
I 3577022
 
6.1%
U 3548337
 
6.0%
Other values (14) 6593716
11.2%
None
ValueCountFrequency (%)
Í 4014794
72.5%
Á 837244
 
15.1%
Ě 272989
 
4.9%
Ř 245745
 
4.4%
Ý 56537
 
1.0%
É 44698
 
0.8%
Č 33319
 
0.6%
Š 18503
 
0.3%
Ů 10935
 
0.2%
Ž 116
 
< 0.1%

ObchOznTyp
Categorical

Distinct72999
Distinct (%)1.8%
Missing6823
Missing (%)0.2%
Memory size62.0 MiB
OCTAVIA
 
133697
FABIA
 
121616
OCTAVIA (1Z)
 
96572
FABIA (5J)
 
81984
FABIA (6Y)
 
70818
Other values (72994)
3549617 

Length

Max length40
Median length34
Mean length8.0538366
Min length1

Characters and Unicode

Total characters32652702
Distinct characters118
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37460 ?
Unique (%)0.9%

Sample

1st rowPS2 10.08 AGRO
2nd rowMV 2-028
3rd rowP93S
4th rowPS2 09.07 AGRO
5th rowP73S

Common Values

ValueCountFrequency (%)
OCTAVIA 133697
 
3.3%
FABIA 121616
 
3.0%
OCTAVIA (1Z) 96572
 
2.4%
FABIA (5J) 81984
 
2.0%
FABIA (6Y) 70818
 
1.7%
OCTAVIA (5E) 62425
 
1.5%
FELICIA 59442
 
1.5%
OCTAVIA (1U) 55824
 
1.4%
GOLF 42401
 
1.0%
SUPERB (3T) 38813
 
1.0%
Other values (72989) 3290712
81.0%

Length

2023-04-01T10:27:05.394540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
octavia 392941
 
6.1%
fabia 335059
 
5.2%
combi 115455
 
1.8%
1z 110565
 
1.7%
6y 107988
 
1.7%
5j 106415
 
1.6%
golf 97357
 
1.5%
passat 88867
 
1.4%
focus 77049
 
1.2%
felicia 73204
 
1.1%
Other values (38485) 4954605
76.7%

Most occurring characters

ValueCountFrequency (%)
A 3604848
 
11.0%
2530443
 
7.7%
I 1835358
 
5.6%
O 1716997
 
5.3%
T 1565166
 
4.8%
C 1452209
 
4.4%
( 1384505
 
4.2%
) 1384205
 
4.2%
R 1331285
 
4.1%
S 1273183
 
3.9%
Other values (108) 14574503
44.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22650100
69.4%
Decimal Number 4312640
 
13.2%
Space Separator 2530443
 
7.7%
Open Punctuation 1384505
 
4.2%
Close Punctuation 1384205
 
4.2%
Dash Punctuation 156543
 
0.5%
Other Punctuation 118264
 
0.4%
Lowercase Letter 92069
 
0.3%
Modifier Symbol 22695
 
0.1%
Math Symbol 1233
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3604848
15.9%
I 1835358
 
8.1%
O 1716997
 
7.6%
T 1565166
 
6.9%
C 1452209
 
6.4%
R 1331285
 
5.9%
S 1273183
 
5.6%
E 1232457
 
5.4%
N 929292
 
4.1%
F 857418
 
3.8%
Other values (35) 6851887
30.3%
Lowercase Letter
ValueCountFrequency (%)
i 48914
53.1%
a 5675
 
6.2%
r 4774
 
5.2%
o 4114
 
4.5%
e 2977
 
3.2%
n 2880
 
3.1%
x 2788
 
3.0%
t 2246
 
2.4%
s 1873
 
2.0%
d 1623
 
1.8%
Other values (30) 14205
 
15.4%
Other Punctuation
ValueCountFrequency (%)
. 68289
57.7%
/ 43807
37.0%
* 2848
 
2.4%
, 2122
 
1.8%
! 1111
 
0.9%
' 65
 
0.1%
& 10
 
< 0.1%
; 4
 
< 0.1%
: 3
 
< 0.1%
@ 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 842757
19.5%
1 690473
16.0%
5 552069
12.8%
3 530223
12.3%
2 496846
11.5%
6 388412
9.0%
4 287331
 
6.7%
7 223583
 
5.2%
8 196759
 
4.6%
9 104187
 
2.4%
Modifier Symbol
ValueCountFrequency (%)
´ 22691
> 99.9%
¨ 3
 
< 0.1%
` 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 156539
> 99.9%
4
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 1232
99.9%
| 1
 
0.1%
Space Separator
ValueCountFrequency (%)
2530443
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1384505
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1384205
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22742169
69.6%
Common 9910533
30.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3604848
15.9%
I 1835358
 
8.1%
O 1716997
 
7.5%
T 1565166
 
6.9%
C 1452209
 
6.4%
R 1331285
 
5.9%
S 1273183
 
5.6%
E 1232457
 
5.4%
N 929292
 
4.1%
F 857418
 
3.8%
Other values (75) 6943956
30.5%
Common
ValueCountFrequency (%)
2530443
25.5%
( 1384505
14.0%
) 1384205
14.0%
0 842757
 
8.5%
1 690473
 
7.0%
5 552069
 
5.6%
3 530223
 
5.4%
2 496846
 
5.0%
6 388412
 
3.9%
4 287331
 
2.9%
Other values (23) 823269
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32563352
99.7%
None 89346
 
0.3%
Punctuation 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3604848
 
11.1%
2530443
 
7.8%
I 1835358
 
5.6%
O 1716997
 
5.3%
T 1565166
 
4.8%
C 1452209
 
4.5%
( 1384505
 
4.3%
) 1384205
 
4.3%
R 1331285
 
4.1%
S 1273183
 
3.9%
Other values (72) 14485153
44.5%
None
ValueCountFrequency (%)
Ý 23041
25.8%
´ 22691
25.4%
Í 21858
24.5%
Á 16172
18.1%
É 1952
 
2.2%
á 1028
 
1.2%
Č 527
 
0.6%
í 495
 
0.6%
ý 445
 
0.5%
Š 375
 
0.4%
Other values (25) 762
 
0.9%
Punctuation
ValueCountFrequency (%)
4
100.0%

Ct
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct142
Distinct (%)< 0.1%
Missing11
Missing (%)< 0.1%
Memory size62.0 MiB
M1
2901542 
N1
295972 
O1
 
141933
N3
 
123286
O4
 
88733
Other values (137)
509650 

Length

Max length7
Median length2
Mean length2.0683472
Min length1

Characters and Unicode

Total characters8399798
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowOT4
2nd rowOT4
3rd rowOT4
4th rowOT4
5th rowOT4

Common Values

ValueCountFrequency (%)
M1 2901542
71.4%
N1 295972
 
7.3%
O1 141933
 
3.5%
N3 123286
 
3.0%
O4 88733
 
2.2%
M1G 83648
 
2.1%
L3e 64852
 
1.6%
LC 62127
 
1.5%
N2 60415
 
1.5%
O2 47504
 
1.2%
Other values (132) 191104
 
4.7%

Length

2023-04-01T10:27:05.495466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1 2901542
71.4%
n1 295972
 
7.3%
o1 141933
 
3.5%
n3 123286
 
3.0%
o4 88733
 
2.2%
m1g 83648
 
2.1%
l3e 64852
 
1.6%
lc 62127
 
1.5%
n2 60415
 
1.5%
o2 47504
 
1.2%
Other values (129) 191104
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 3502672
41.7%
M 3006213
35.8%
N 529985
 
6.3%
O 304343
 
3.6%
3 251341
 
3.0%
L 163217
 
1.9%
G 132120
 
1.6%
2 117033
 
1.4%
4 108504
 
1.3%
e 83486
 
1.0%
Other values (25) 200884
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4312696
51.3%
Decimal Number 3984588
47.4%
Lowercase Letter 93073
 
1.1%
Dash Punctuation 8930
 
0.1%
Space Separator 370
 
< 0.1%
Other Punctuation 141
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 3006213
69.7%
N 529985
 
12.3%
O 304343
 
7.1%
L 163217
 
3.8%
G 132120
 
3.1%
T 67086
 
1.6%
C 62202
 
1.4%
A 19812
 
0.5%
S 10359
 
0.2%
R 6388
 
0.1%
Other values (7) 10971
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 3502672
87.9%
3 251341
 
6.3%
2 117033
 
2.9%
4 108504
 
2.7%
7 3767
 
0.1%
5 864
 
< 0.1%
6 407
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 83486
89.7%
a 8645
 
9.3%
b 877
 
0.9%
z 30
 
< 0.1%
n 29
 
< 0.1%
s 4
 
< 0.1%
p 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 138
97.9%
* 3
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 8930
100.0%
Space Separator
ValueCountFrequency (%)
370
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4405769
52.5%
Common 3994029
47.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 3006213
68.2%
N 529985
 
12.0%
O 304343
 
6.9%
L 163217
 
3.7%
G 132120
 
3.0%
e 83486
 
1.9%
T 67086
 
1.5%
C 62202
 
1.4%
A 19812
 
0.4%
S 10359
 
0.2%
Other values (14) 26946
 
0.6%
Common
ValueCountFrequency (%)
1 3502672
87.7%
3 251341
 
6.3%
2 117033
 
2.9%
4 108504
 
2.7%
- 8930
 
0.2%
7 3767
 
0.1%
5 864
 
< 0.1%
6 407
 
< 0.1%
370
 
< 0.1%
. 138
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8399798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3502672
41.7%
M 3006213
35.8%
N 529985
 
6.3%
O 304343
 
3.6%
3 251341
 
3.0%
L 163217
 
1.9%
G 132120
 
1.6%
2 117033
 
1.4%
4 108504
 
1.3%
e 83486
 
1.0%
Other values (25) 200884
 
2.4%
Distinct22106
Distinct (%)0.5%
Missing34049
Missing (%)0.8%
Memory size62.0 MiB
Minimum1753-03-13 00:00:00
Maximum2030-12-27 00:00:00
2023-04-01T10:27:05.589885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:27:05.688844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Km
Real number (ℝ)

Distinct522657
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163719.43
Minimum0
Maximum9999999
Zeros333034
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:05.790926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q162946
median149158
Q3229778
95-th percentile376488.7
Maximum9999999
Range9999999
Interquartile range (IQR)166832

Descriptive statistics

Standard deviation145805.56
Coefficient of variation (CV)0.89058193
Kurtosis99.279102
Mean163719.43
Median Absolute Deviation (MAD)83433
Skewness4.3421113
Sum6.6488538 × 1011
Variance2.1259262 × 1010
MonotonicityNot monotonic
2023-04-01T10:27:05.895465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 333034
 
8.2%
1 2115
 
0.1%
5 681
 
< 0.1%
14 675
 
< 0.1%
9 660
 
< 0.1%
11 630
 
< 0.1%
13 629
 
< 0.1%
7 628
 
< 0.1%
10 625
 
< 0.1%
15 615
 
< 0.1%
Other values (522647) 3720835
91.6%
ValueCountFrequency (%)
0 333034
8.2%
1 2115
 
0.1%
2 450
 
< 0.1%
3 505
 
< 0.1%
4 511
 
< 0.1%
5 681
 
< 0.1%
6 586
 
< 0.1%
7 628
 
< 0.1%
8 601
 
< 0.1%
9 660
 
< 0.1%
ValueCountFrequency (%)
9999999 1
< 0.1%
9987538 1
< 0.1%
9776784 1
< 0.1%
9096347 1
< 0.1%
8999222 1
< 0.1%
8815127 1
< 0.1%
8769140 1
< 0.1%
8729745 1
< 0.1%
8678886 1
< 0.1%
8601027 1
< 0.1%

VyslSTK
Categorical

Distinct3
Distinct (%)< 0.1%
Missing70
Missing (%)< 0.1%
Memory size62.0 MiB
způsobilé
3797518 
částečně způsobilé
 
237960
nezpůsobilé
 
25579

Length

Max length18
Median length9
Mean length9.5399575
Min length9

Characters and Unicode

Total characters38742311
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowzpůsobilé
2nd rowzpůsobilé
3rd rowzpůsobilé
4th rowzpůsobilé
5th rowzpůsobilé

Common Values

ValueCountFrequency (%)
způsobilé 3797518
93.5%
částečně způsobilé 237960
 
5.9%
nezpůsobilé 25579
 
0.6%
(Missing) 70
 
< 0.1%

Length

2023-04-01T10:27:05.984880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T10:27:06.072479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
způsobilé 4035478
93.9%
částečně 237960
 
5.5%
nezpůsobilé 25579
 
0.6%

Most occurring characters

ValueCountFrequency (%)
s 4299017
11.1%
z 4061057
10.5%
p 4061057
10.5%
ů 4061057
10.5%
o 4061057
10.5%
b 4061057
10.5%
i 4061057
10.5%
l 4061057
10.5%
é 4061057
10.5%
č 475920
 
1.2%
Other values (6) 1478918
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38504351
99.4%
Space Separator 237960
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 4299017
11.2%
z 4061057
10.5%
p 4061057
10.5%
ů 4061057
10.5%
o 4061057
10.5%
b 4061057
10.5%
i 4061057
10.5%
l 4061057
10.5%
é 4061057
10.5%
č 475920
 
1.2%
Other values (5) 1240958
 
3.2%
Space Separator
ValueCountFrequency (%)
237960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38504351
99.4%
Common 237960
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 4299017
11.2%
z 4061057
10.5%
p 4061057
10.5%
ů 4061057
10.5%
o 4061057
10.5%
b 4061057
10.5%
i 4061057
10.5%
l 4061057
10.5%
é 4061057
10.5%
č 475920
 
1.2%
Other values (5) 1240958
 
3.2%
Common
ValueCountFrequency (%)
237960
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29668357
76.6%
None 9073954
 
23.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 4299017
14.5%
z 4061057
13.7%
p 4061057
13.7%
o 4061057
13.7%
b 4061057
13.7%
i 4061057
13.7%
l 4061057
13.7%
e 263539
 
0.9%
n 263539
 
0.9%
t 237960
 
0.8%
None
ValueCountFrequency (%)
ů 4061057
44.8%
é 4061057
44.8%
č 475920
 
5.2%
á 237960
 
2.6%
ě 237960
 
2.6%

VyslEmise
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4061127
Missing (%)100.0%
Memory size62.0 MiB

DTKont
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.894518
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:06.137291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3961761
Coefficient of variation (CV)0.48235185
Kurtosis-1.0891519
Mean2.894518
Median Absolute Deviation (MAD)1
Skewness0.14666962
Sum11755005
Variance1.9493077
MonotonicityNot monotonic
2023-04-01T10:27:06.198992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 885347
21.8%
1 871944
21.5%
2 838439
20.6%
4 826632
20.4%
5 591335
14.6%
6 45071
 
1.1%
7 2359
 
0.1%
ValueCountFrequency (%)
1 871944
21.5%
2 838439
20.6%
3 885347
21.8%
4 826632
20.4%
5 591335
14.6%
6 45071
 
1.1%
7 2359
 
0.1%
ValueCountFrequency (%)
7 2359
 
0.1%
6 45071
 
1.1%
5 591335
14.6%
4 826632
20.4%
3 885347
21.8%
2 838439
20.6%
1 871944
21.5%

ZavA
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6846563
Minimum0
Maximum32
Zeros2004573
Zeros (%)49.4%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:06.280108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum32
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2843603
Coefficient of variation (CV)1.3559801
Kurtosis2.9738081
Mean1.6846563
Median Absolute Deviation (MAD)1
Skewness1.6002806
Sum6841603
Variance5.2183019
MonotonicityNot monotonic
2023-04-01T10:27:06.533408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 2004573
49.4%
1 476687
 
11.7%
2 416117
 
10.2%
3 357129
 
8.8%
4 291220
 
7.2%
5 210213
 
5.2%
6 125243
 
3.1%
7 75788
 
1.9%
8 44977
 
1.1%
9 27825
 
0.7%
Other values (22) 31355
 
0.8%
ValueCountFrequency (%)
0 2004573
49.4%
1 476687
 
11.7%
2 416117
 
10.2%
3 357129
 
8.8%
4 291220
 
7.2%
5 210213
 
5.2%
6 125243
 
3.1%
7 75788
 
1.9%
8 44977
 
1.1%
9 27825
 
0.7%
ValueCountFrequency (%)
32 2
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
27 5
 
< 0.1%
26 5
 
< 0.1%
25 8
 
< 0.1%
24 10
 
< 0.1%
23 20
< 0.1%
22 27
< 0.1%

ZavB
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15495009
Minimum0
Maximum33
Zeros3805454
Zeros (%)93.7%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:06.624005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77394005
Coefficient of variation (CV)4.9947699
Kurtosis81.670606
Mean0.15495009
Median Absolute Deviation (MAD)0
Skewness7.5987937
Sum629272
Variance0.5989832
MonotonicityNot monotonic
2023-04-01T10:27:06.710612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 3805454
93.7%
1 108376
 
2.7%
2 56684
 
1.4%
3 36447
 
0.9%
4 22041
 
0.5%
5 13073
 
0.3%
6 7576
 
0.2%
7 4461
 
0.1%
8 2681
 
0.1%
9 1692
 
< 0.1%
Other values (20) 2642
 
0.1%
ValueCountFrequency (%)
0 3805454
93.7%
1 108376
 
2.7%
2 56684
 
1.4%
3 36447
 
0.9%
4 22041
 
0.5%
5 13073
 
0.3%
6 7576
 
0.2%
7 4461
 
0.1%
8 2681
 
0.1%
9 1692
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
28 3
 
< 0.1%
27 2
 
< 0.1%
26 2
 
< 0.1%
25 3
 
< 0.1%
24 6
 
< 0.1%
23 5
 
< 0.1%
22 2
 
< 0.1%
21 11
< 0.1%
20 19
< 0.1%

ZavC
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.010717222
Minimum0
Maximum19
Zeros4026648
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:06.800617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1320191
Coefficient of variation (CV)12.318407
Kurtosis771.05051
Mean0.010717222
Median Absolute Deviation (MAD)0
Skewness19.814761
Sum43524
Variance0.017429044
MonotonicityNot monotonic
2023-04-01T10:27:06.879102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 4026648
99.2%
1 28176
 
0.7%
2 4537
 
0.1%
3 1207
 
< 0.1%
4 366
 
< 0.1%
5 112
 
< 0.1%
6 33
 
< 0.1%
7 19
 
< 0.1%
8 11
 
< 0.1%
10 6
 
< 0.1%
Other values (6) 12
 
< 0.1%
ValueCountFrequency (%)
0 4026648
99.2%
1 28176
 
0.7%
2 4537
 
0.1%
3 1207
 
< 0.1%
4 366
 
< 0.1%
5 112
 
< 0.1%
6 33
 
< 0.1%
7 19
 
< 0.1%
8 11
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
16 2
 
< 0.1%
13 1
 
< 0.1%
12 4
 
< 0.1%
11 1
 
< 0.1%
10 6
 
< 0.1%
9 3
 
< 0.1%
8 11
 
< 0.1%
7 19
< 0.1%
6 33
< 0.1%

Zav0
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.07568835
Minimum0
Maximum6
Zeros3777381
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:06.964905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.28679488
Coefficient of variation (CV)3.7891549
Kurtosis18.165856
Mean0.07568835
Median Absolute Deviation (MAD)0
Skewness4.0508186
Sum307380
Variance0.082251305
MonotonicityNot monotonic
2023-04-01T10:27:07.038196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3777381
93.0%
1 261348
 
6.4%
2 21243
 
0.5%
3 1081
 
< 0.1%
4 68
 
< 0.1%
5 5
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 3777381
93.0%
1 261348
 
6.4%
2 21243
 
0.5%
3 1081
 
< 0.1%
4 68
 
< 0.1%
5 5
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 5
 
< 0.1%
4 68
 
< 0.1%
3 1081
 
< 0.1%
2 21243
 
0.5%
1 261348
 
6.4%
0 3777381
93.0%

Zav1
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40424887
Minimum0
Maximum13
Zeros2972682
Zeros (%)73.2%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:07.117169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78394484
Coefficient of variation (CV)1.9392629
Kurtosis7.0532239
Mean0.40424887
Median Absolute Deviation (MAD)0
Skewness2.3596366
Sum1641706
Variance0.61456951
MonotonicityNot monotonic
2023-04-01T10:27:07.204523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 2972682
73.2%
1 685708
 
16.9%
2 290739
 
7.2%
3 84449
 
2.1%
4 20162
 
0.5%
5 5069
 
0.1%
6 1510
 
< 0.1%
7 525
 
< 0.1%
8 170
 
< 0.1%
9 70
 
< 0.1%
Other values (4) 43
 
< 0.1%
ValueCountFrequency (%)
0 2972682
73.2%
1 685708
 
16.9%
2 290739
 
7.2%
3 84449
 
2.1%
4 20162
 
0.5%
5 5069
 
0.1%
6 1510
 
< 0.1%
7 525
 
< 0.1%
8 170
 
< 0.1%
9 70
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 7
 
< 0.1%
11 8
 
< 0.1%
10 27
 
< 0.1%
9 70
 
< 0.1%
8 170
 
< 0.1%
7 525
 
< 0.1%
6 1510
 
< 0.1%
5 5069
 
0.1%
4 20162
0.5%

Zav2
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084917315
Minimum0
Maximum7
Zeros3745528
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:07.287369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30535549
Coefficient of variation (CV)3.5959155
Kurtosis17.244258
Mean0.084917315
Median Absolute Deviation (MAD)0
Skewness3.8947704
Sum344860
Variance0.093241976
MonotonicityNot monotonic
2023-04-01T10:27:07.362573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3745528
92.2%
1 288417
 
7.1%
2 25280
 
0.6%
3 1748
 
< 0.1%
4 136
 
< 0.1%
5 14
 
< 0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 3745528
92.2%
1 288417
 
7.1%
2 25280
 
0.6%
3 1748
 
< 0.1%
4 136
 
< 0.1%
5 14
 
< 0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 3
 
< 0.1%
5 14
 
< 0.1%
4 136
 
< 0.1%
3 1748
 
< 0.1%
2 25280
 
0.6%
1 288417
 
7.1%
0 3745528
92.2%

Zav3
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.085319174
Minimum0
Maximum7
Zeros3760669
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:07.439889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32319677
Coefficient of variation (CV)3.7880907
Kurtosis24.694222
Mean0.085319174
Median Absolute Deviation (MAD)0
Skewness4.4536712
Sum346492
Variance0.10445615
MonotonicityNot monotonic
2023-04-01T10:27:07.510102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3760669
92.6%
1 260934
 
6.4%
2 33947
 
0.8%
3 4780
 
0.1%
4 683
 
< 0.1%
5 97
 
< 0.1%
6 12
 
< 0.1%
7 5
 
< 0.1%
ValueCountFrequency (%)
0 3760669
92.6%
1 260934
 
6.4%
2 33947
 
0.8%
3 4780
 
0.1%
4 683
 
< 0.1%
5 97
 
< 0.1%
6 12
 
< 0.1%
7 5
 
< 0.1%
ValueCountFrequency (%)
7 5
 
< 0.1%
6 12
 
< 0.1%
5 97
 
< 0.1%
4 683
 
< 0.1%
3 4780
 
0.1%
2 33947
 
0.8%
1 260934
 
6.4%
0 3760669
92.6%

Zav4
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31851922
Minimum0
Maximum18
Zeros3132361
Zeros (%)77.1%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:07.591630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68469682
Coefficient of variation (CV)2.1496248
Kurtosis12.402732
Mean0.31851922
Median Absolute Deviation (MAD)0
Skewness2.883486
Sum1293547
Variance0.46880974
MonotonicityNot monotonic
2023-04-01T10:27:07.669287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 3132361
77.1%
1 662394
 
16.3%
2 198312
 
4.9%
3 48344
 
1.2%
4 13125
 
0.3%
5 4178
 
0.1%
6 1468
 
< 0.1%
7 570
 
< 0.1%
8 223
 
< 0.1%
9 79
 
< 0.1%
Other values (8) 73
 
< 0.1%
ValueCountFrequency (%)
0 3132361
77.1%
1 662394
 
16.3%
2 198312
 
4.9%
3 48344
 
1.2%
4 13125
 
0.3%
5 4178
 
0.1%
6 1468
 
< 0.1%
7 570
 
< 0.1%
8 223
 
< 0.1%
9 79
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 5
 
< 0.1%
12 11
 
< 0.1%
11 14
 
< 0.1%
10 37
 
< 0.1%
9 79
 
< 0.1%
8 223
< 0.1%

Zav5
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35544099
Minimum0
Maximum10
Zeros2934360
Zeros (%)72.3%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:07.752858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.64501664
Coefficient of variation (CV)1.814694
Kurtosis4.8966295
Mean0.35544099
Median Absolute Deviation (MAD)0
Skewness2.0213813
Sum1443491
Variance0.41604647
MonotonicityNot monotonic
2023-04-01T10:27:07.827765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 2934360
72.3%
1 861764
 
21.2%
2 221822
 
5.5%
3 36553
 
0.9%
4 5207
 
0.1%
5 1077
 
< 0.1%
6 246
 
< 0.1%
7 63
 
< 0.1%
8 24
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
0 2934360
72.3%
1 861764
 
21.2%
2 221822
 
5.5%
3 36553
 
0.9%
4 5207
 
0.1%
5 1077
 
< 0.1%
6 246
 
< 0.1%
7 63
 
< 0.1%
8 24
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
10 3
 
< 0.1%
9 8
 
< 0.1%
8 24
 
< 0.1%
7 63
 
< 0.1%
6 246
 
< 0.1%
5 1077
 
< 0.1%
4 5207
 
0.1%
3 36553
 
0.9%
2 221822
 
5.5%
1 861764
21.2%

Zav6
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74653267
Minimum0
Maximum20
Zeros2370789
Zeros (%)58.4%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:07.908961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1108819
Coefficient of variation (CV)1.4880552
Kurtosis4.6726751
Mean0.74653267
Median Absolute Deviation (MAD)0
Skewness1.8510352
Sum3031764
Variance1.2340585
MonotonicityNot monotonic
2023-04-01T10:27:07.993201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 2370789
58.4%
1 847547
 
20.9%
2 519593
 
12.8%
3 211576
 
5.2%
4 72368
 
1.8%
5 24624
 
0.6%
6 8861
 
0.2%
7 3467
 
0.1%
8 1328
 
< 0.1%
9 520
 
< 0.1%
Other values (10) 454
 
< 0.1%
ValueCountFrequency (%)
0 2370789
58.4%
1 847547
 
20.9%
2 519593
 
12.8%
3 211576
 
5.2%
4 72368
 
1.8%
5 24624
 
0.6%
6 8861
 
0.2%
7 3467
 
0.1%
8 1328
 
< 0.1%
9 520
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
17 2
 
< 0.1%
16 6
 
< 0.1%
15 4
 
< 0.1%
14 14
 
< 0.1%
13 26
 
< 0.1%
12 47
 
< 0.1%
11 115
< 0.1%
10 238
< 0.1%

Zav7
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013660001
Minimum0
Maximum7
Zeros4009463
Zeros (%)98.7%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:08.076292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.12491932
Coefficient of variation (CV)9.1448984
Kurtosis136.17047
Mean0.013660001
Median Absolute Deviation (MAD)0
Skewness10.436588
Sum55475
Variance0.015604838
MonotonicityNot monotonic
2023-04-01T10:27:08.144392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4009463
98.7%
1 48278
 
1.2%
2 3037
 
0.1%
3 284
 
< 0.1%
4 58
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 4009463
98.7%
1 48278
 
1.2%
2 3037
 
0.1%
3 284
 
< 0.1%
4 58
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
< 0.1%
5 4
 
< 0.1%
4 58
 
< 0.1%
3 284
 
< 0.1%
2 3037
 
0.1%
1 48278
 
1.2%
0 4009463
98.7%

Zav8
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012891249
Minimum0
Maximum7
Zeros4021365
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:08.218699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.14315329
Coefficient of variation (CV)11.104687
Kurtosis244.86854
Mean0.012891249
Median Absolute Deviation (MAD)0
Skewness14.012971
Sum52353
Variance0.020492864
MonotonicityNot monotonic
2023-04-01T10:27:08.284912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4021365
99.0%
1 29774
 
0.7%
2 7873
 
0.2%
3 1707
 
< 0.1%
4 338
 
< 0.1%
5 61
 
< 0.1%
6 8
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 4021365
99.0%
1 29774
 
0.7%
2 7873
 
0.2%
3 1707
 
< 0.1%
4 338
 
< 0.1%
5 61
 
< 0.1%
6 8
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 8
 
< 0.1%
5 61
 
< 0.1%
4 338
 
< 0.1%
3 1707
 
< 0.1%
2 7873
 
0.2%
1 29774
 
0.7%
0 4021365
99.0%

Zav9
Real number (ℝ)

SKEWED  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0010386279
Minimum0
Maximum6
Zeros4058109
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size62.0 MiB
2023-04-01T10:27:08.358903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.042909992
Coefficient of variation (CV)41.314113
Kurtosis3658.029
Mean0.0010386279
Median Absolute Deviation (MAD)0
Skewness53.93149
Sum4218
Variance0.0018412674
MonotonicityNot monotonic
2023-04-01T10:27:08.422511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4058109
99.9%
1 2152
 
0.1%
2 609
 
< 0.1%
3 199
 
< 0.1%
4 41
 
< 0.1%
5 15
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 4058109
99.9%
1 2152
 
0.1%
2 609
 
< 0.1%
3 199
 
< 0.1%
4 41
 
< 0.1%
5 15
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 15
 
< 0.1%
4 41
 
< 0.1%
3 199
 
< 0.1%
2 609
 
< 0.1%
1 2152
 
0.1%
0 4058109
99.9%

StariDnu
Real number (ℝ)

Distinct22106
Distinct (%)0.5%
Missing34049
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean5913.433
Minimum-2907
Maximum98554
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)< 0.1%
Memory size62.0 MiB
2023-04-01T10:27:08.515330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2907
5-th percentile2030
Q13747
median5635
Q37464
95-th percentile11333
Maximum98554
Range101461
Interquartile range (IQR)3717

Descriptive statistics

Standard deviation3147.5827
Coefficient of variation (CV)0.53227672
Kurtosis8.4553343
Mean5913.433
Median Absolute Deviation (MAD)1857
Skewness1.7879179
Sum2.3813856 × 1010
Variance9907276.8
MonotonicityNot monotonic
2023-04-01T10:27:08.620744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8776 6521
 
0.2%
9141 6357
 
0.2%
9506 6337
 
0.2%
8411 5655
 
0.1%
9872 4742
 
0.1%
10237 3595
 
0.1%
12063 3426
 
0.1%
8045 3359
 
0.1%
12428 3218
 
0.1%
10602 2992
 
0.1%
Other values (22096) 3980876
98.0%
(Missing) 34049
 
0.8%
ValueCountFrequency (%)
-2907 2
< 0.1%
-2893 1
< 0.1%
-2831 1
< 0.1%
-2808 1
< 0.1%
-2804 1
< 0.1%
-2749 1
< 0.1%
-2746 1
< 0.1%
-2726 1
< 0.1%
-2646 1
< 0.1%
-2592 1
< 0.1%
ValueCountFrequency (%)
98554 1
 
< 0.1%
98520 1
 
< 0.1%
98361 1
 
< 0.1%
98334 1
 
< 0.1%
66744 1
 
< 0.1%
66658 1
 
< 0.1%
54110 1
 
< 0.1%
44935 382
< 0.1%
44925 1
 
< 0.1%
44919 1
 
< 0.1%

Interactions

2023-04-01T10:26:33.410980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:24.320516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:29.342567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:34.050373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:38.563956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:42.851057image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:47.363120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:51.693600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:56.196160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:00.677581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:05.247065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:09.943855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:14.631499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:19.214054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:23.589019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:28.320854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:33.815498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:24.629562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:29.625502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:34.327622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:38.829044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:43.113621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:47.626358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:51.971439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:56.456873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:00.958758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:05.527295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:10.278708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:14.921762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:19.472599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:23.851157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:28.636715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:34.226137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:24.973107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:29.924754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:34.608772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:39.094195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:43.375984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:47.895523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:52.246389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:56.721595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:01.226522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:05.810466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:10.728299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:15.204874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:19.734382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:24.114148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:28.935405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:34.617029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:25.320940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:30.228578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:34.885016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:39.350293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:43.642536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:48.161806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:52.523011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:56.988483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:01.502129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:06.088904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:11.014778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:15.488168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:20.000687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:24.393103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:29.238186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:34.999684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:25.636316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:30.534385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:35.157511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:39.613208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:43.897678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:48.428166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:52.797478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:57.249819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:01.764948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:06.367201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:11.283478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:15.768939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:20.259833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:24.668322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:29.542039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:35.381339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:25.941377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:30.835799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:35.435235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:39.884973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:44.387699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:48.690783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:53.078357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:57.641488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:02.033542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:06.645648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:11.565348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:16.056274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:20.527591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:24.948865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:29.886129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:35.755502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:26.244685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:31.135943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:35.712409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:40.148022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:44.650504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:48.962997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:53.357423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:57.903494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:02.310478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:06.927220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:11.895151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:16.336666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:20.788481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:25.228671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:30.196788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:36.150785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:26.548723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:31.433624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:36.022325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:40.412514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:44.917849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:49.228642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:53.632942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:58.161745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:02.587333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:07.204136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:12.161788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:16.622674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:21.052600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:25.552908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:30.496252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:36.539780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:26.849050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:31.737587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:36.302370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:40.679647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:45.185128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:49.502523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:53.912912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:58.424737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:02.857809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:07.486288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:12.428092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:16.910259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:21.316313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:25.865586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:30.822631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:36.948774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:27.155854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:32.036548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:36.588634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:40.947743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:45.453232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:49.774211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:54.190418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:58.691228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:03.152235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:07.757722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:12.695582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:17.194715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:21.580905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:26.172209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:31.165375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:37.328187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:27.461284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:32.344178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:36.871930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:41.215532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:45.719945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:50.042142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:54.476286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:58.960646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:03.449553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:08.076164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:12.969660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:17.483436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:21.846542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:26.477869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:31.463594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:37.703638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:27.768005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:32.637156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:37.145762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:41.478228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:45.981580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:50.303744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:54.749151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:59.221253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:03.772712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:08.359894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:13.232813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:17.760931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:22.104429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:26.773860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:31.771758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:38.098288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:28.076777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:32.933422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:37.421421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:41.748816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:46.250200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:50.573458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:55.034685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:59.492870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:04.069018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:08.669290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:13.504565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:18.050301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:22.362912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:27.085821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:32.070085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:38.620278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:28.381542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:33.197147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:37.696537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:42.016232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:46.522340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:50.843640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:55.325766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:59.801344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:04.352293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:08.969634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:13.772175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:18.334290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:22.625722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:27.379043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:32.364017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:39.011796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:28.687185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:33.461242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:37.971683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:42.279272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:46.787380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:51.109380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:55.613130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:00.081655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:04.622766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:09.255350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:14.043176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:18.620505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:22.889008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:27.680234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:32.641928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:39.365168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:29.041584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:33.768662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:38.295670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:42.587116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:47.093023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:51.416143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:25:55.934381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:00.406505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:04.948104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:09.583335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:14.353020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:18.948284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:23.324802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:28.020533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:26:33.015581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-01T10:26:41.751670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-01T10:26:47.247092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-01T10:26:58.528955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKVyslEmiseDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu
STK
6711pravidelná78572020-01-01NaNBSSPŘÍVĚS TRAKTOROVÝPS2 10.08 AGROOT41988-03-210způsobiléNaN3400010000300012714.0
6711pravidelná116662020-01-01NaNSTSPŘÍVĚS TRAKTOROVÝMV 2-028OT41986-02-240způsobiléNaN3400110020500013470.0
6711pravidelná9193272020-01-01NaNBSSPŘÍVĚS TRAKTOROVÝP93SOT41976-08-020způsobiléNaN3300200000210016963.0
6711pravidelná11472020-01-01NaNBSSPŘÍVĚS TRAKTOROVÝPS2 09.07 AGROOT41988-10-190způsobiléNaN3000100000010012502.0
6711pravidelná7317132020-01-01NaNBSSPŘÍVĚS TRAKTOROVÝP73SOT41970-08-140způsobiléNaN3500110020300019143.0
6711pravidelná06582020-01-01NaNBSSPŘÍVĚS TRAKTOROVÝPS2 10.08 AGROOT41985-03-130způsobiléNaN31100220030810013818.0
6711pravidelná152572020-01-01NaNJ.Z.P.M.L. JAROCINIEPŘÍVĚS TRAKTOROVÝPA - 3,5OT31965-12-060způsobiléNaN3500110000400020855.0
6711pravidelnáTK9NT90403VNV54602020-01-01NaNZDTNÁVĚS TRAKTOROVÝNS 9OT42003-03-310způsobiléNaN300000000000007226.0
6711pravidelnáZ8BDO79172020-01-01667TA/MEKNEW HOLLANDTRAKTOR KOLOVÝT 6050T12008-06-020způsobiléNaN300000000000005336.0
6711pravidelná31612020-01-01Z 5201ZETORTRAKTOR KOLOVÝ5211T11984-11-270způsobiléNaN3400000020120013924.0
DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKVyslEmiseDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu
STK
3114Evidenční kontrolaWF0LXXGBVLTL042792020-12-314HBFORDNÁKLADNÍ AUTOMOBILTRANSITN11996-12-23296199způsobiléNaN400000000000009515.0
3609pravidelnáWV2ZZZ2KZ9X0544922020-12-31BSXVWOSOBNÍ AUTOMOBILCADDY LIFE (2K)M12008-11-03229772způsobiléNaN430000000210005182.0
3114pravidelnáYV1MC7551AJ0993302020-12-31D4204TVOLVOOSOBNÍ AUTOMOBILC 70 (M)M12010-04-12167574způsobiléNaN400000000000004657.0
3114pravidelnáWAUZZZ8T7FA0309192020-12-31CGLAUDIOSOBNÍ AUTOMOBILA5M12014-12-1774809způsobiléNaN400000000000002947.0
3521pravidelnáMD301A1BR1C8323222020-12-31NaNHEROMOTOCYKLPUCHLC2002-05-1543306způsobiléNaN400000000000007546.0
3521Evidenční kontrola5822162020-12-31638 314JAWAMOTOCYKL350L3e1991-05-0819816způsobiléNaN4000000000000011571.0
3521pravidelnáWF0WXXGCDW7S852682020-12-31SHDAFORDOSOBNÍ AUTOMOBILFOCUS (DA3)M12007-08-16199728způsobiléNaN400000000000005627.0
3609pravidelnáWF0KXXBDFKYS384272020-12-31D2FAFORDNÁKLADNÍ AUTOMOBILTRANSIT (FSBY)N12002-07-10360545nezpůsobiléNaN468513104370007490.0
3618Na žádost zákazníkaJMZGL6966017133372020-12-31PEMAZDAOSOBNÍ AUTOMOBIL6M12019-12-1621037způsobiléNaN400000000000001122.0
6732Před schvál. tech. způsob. vozidla454040512020-12-31OM 401 LACLAASPRACOVNÍ STROJ SAMOJÍZDNÝ454SSNaT7268způsobiléNaN40000000000000NaN

Duplicate rows

Most frequently occurring

DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu# duplicates
2022Technická silniční kontrola02020-05-15NaNNaNNaNNaNO4NaT0způsobilé50000000000000NaN4
3774pravidelnáTK9KB61206AKP92052020-02-25NaNKOBRASNÁKLADNÍ PŘÍVĚSUNIVERSAL 6O12006-06-060způsobilé200000000000006063.04
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